Data warehouses store data transactions that can be used for various analyses. The data transactions can be associated with a variety of applications, e.g., online shopping applications, and social networking applications. Applications may have to perform various types of analyses for various purposes. For example, some applications may have to analyze the data transactions stored in the data warehouses to determine their most popular selling product for a particular season so that they can stock the product accordingly. In another example, the applications may want to identify the demographic characteristics of the users who buy a specified set of products so that they can recommend related products to those users. Some analyses may require large volumes of granular data transaction and some may not. Since the data that may be needed for future analyses is not predictable, the applications end up storing a large volume of granular data transactions. Some social networking platforms have many millions of users and therefore can generate significant amount of data associated with transactions performed by the users. Storing such data can consume significant data storage resources and can result in increased data storage costs. Further, analyzing large volumes of data, especially in cases when such large volumes of data are not necessary for a set of analyses, can also result in increased consumption of computing resources and/or network resources.